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Abstract

1. Stable isotope analysis is a widely used tool for the reconstruction and interpretation of animal diets and trophic relationships. Analytical tools have improved the robustness of inferring the relative contribution of different prey sources to an animal’s diet by accounting for many of the sources of variation in isotopic data. One major source of uncertainty is Trophic Discrimination Factor (TDF), the change in isotopic signatures between consumers’ tissues and their food sources. This parameter can have a profound impact on model predictions, but often, it is not feasible to estimate a species’ TDF value and so researchers often use aggregated or taxon level estimates, an assumption that in turn has major implications for the interpretation of subsequent analyses.

2. We collected extensive carbon (δ13C) and nitrogen (δ15N) TDF data on mammals and birds from published literature. We then used a Bayesian linear modelling approach to determine if, and to what extent, variation in TDF values can be attributed to a species’ ecology, physiology, phylogenetic relationships and experimental variation. Finally, we developed a Bayesian imputation approach to estimate unknown TDF values and compared the accuracy of this tool using a series of cross-validation tests.

3. Our results show that, for birds and mammals, TDF values are influenced by phylogeny, tissue type sampled, diet of consumer, isotopic signature of food source, and the error associated with the measurement of TDF within a species. Furthermore, our cross-validation tests determined that, our tool can (i) produce accurate estimates of TDF values with a mean distance of 0.2 ‰ from observed TDF values, and (ii) provide an estimate of the precision associated with these estimates, with species presence in the data allowing for a reduced level of uncertainty.

4. By incorporating various sources of variation and reflecting the levels of uncertainty associated with TDF estimates our novel tool will contribute to more accurate and honest reconstructions and interpretations of animal diets and trophic interactions. This tool can be extended readily to include other taxa and sources of variation as data becomes available. To facilitate this, we provide a step-by-step guide and code for this tool: Discrimination Estimation in R (DEsiR)

Author Comment

This is a preprint draft for the package DEsiR. For updates on the package see https://github.com/healyke/DEsiR

Additional Information

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Kevin Healy conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Seán B.A Kelly conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Thomas Guillerme conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Richard Inger conceived and designed the experiments, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Stuart Bearhop conceived and designed the experiments, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Andrew L Jackson conceived and designed the experiments, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Data Deposition

The following information was supplied regarding data availability:

Data is available as part of the DEsiR package https://github.com/healyke/DEsiR

Funding

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